The present invention generally relates to visual pattern recognition (ViPR) and, more particularly, to systems and methods for automatically recognizing merchandise at retailer checkout station based on ViPR.
In many retail store environments, such as in grocery stores, department stores, office supply stores, home improvement stores, and the like, consumers use shopping carts to carry merchandise. A typical shopping cart includes a basket that is designed for storage of the consumer's merchandise and a shelf located beneath the basket. At times, a consumer will use the lower shelf as additional storage space, especially for relatively large and/or bulky merchandise.
On occasion, when using the lower shelf space to carry merchandise, a consumer can leave the store without paying for the merchandise. This may occur because the consumer inadvertently forgets to present the merchandise to the cashier during checkout, or because the consumer intends to defraud the store and steal the merchandise. Similarly, cashiers are sometimes unable to see the bottom of basket (BoB) merchandise, or fail to look for such merchandise, thereby allowing a customer to leave the store without paying for the BoB items. Further, it is known in the retail industry that cashier can sometimes involved in collusion with customers. This collusion can range from fraudulently allowing a customer to take a BoB item without paying to ringing up a substantially lower price item. Cashier fraud is conventionally estimated to constitute around 35% of total grocery retailer “shrink” according to the national supermarket research group 2003/2004 supermarket shrink survey.
Collectively, this type of loss is known in the retail industry as “bottom-of-the-basket” (BoB) loss. Estimates suggest that a typical supermarket can experience between $3,000 to $5,000 of bottom-of-the-basket revenue losses per lane per year. For a typical modern grocery store with 10 checkout lanes, this loss represents $30,000 to $50,000 of unaccounted revenue per year. For a major grocery chain with 1,000 stores, the potential revenue recovery can reach in excess of $50 million dollars annually.
Several efforts have been undertaken to minimize or reduce bottom-of-the-basket losses. These efforts generally fall into three categories: process change and training; lane configuration change; and supplemental detection devices.
Process change and training is aimed at getting cashier and bagger to inspect the cart for BOB items in every transaction. This approach has not been effective because of high personnel turnover, the requirement of constant training, the low skill level of the personnel, a lack of mechanisms for enforcing the new behavior, and a lack of initiative to encourage tracking and preventing collusion.
Lane configuration change is aimed at making the bottom of the basket more visible to the cashier, either by guiding the cart to a separate side of the lane from the customer (called “lane splitting”), or by using a second cart that requires the customer to fully unload his or her cart and reloading the items onto the second cart (called “cart swapping”). Changing the lane configuration is expensive, does not address the collusion, and is typically a more inconvenient, less efficient way to scan and check out items.
Supplemental devices include mirrors placed on the opposite side of the lane to enable the cashier to see BoB items without leaning over or walking around the lane; infrared sensing devices to alert the cashier that there are BoB items; and video surveillance devices to display an image for the cashier to see the BoB. Infrared detection systems, such as those marketed by Kart Saver, Inc. <URL: http://www.kartsaver.com> and Store-Scan, Inc. <URL: http://www.store-scan.com> employ infrared sensors designed to detect the presence of merchandise located on the lower shelf of a shopping cart when the shopping cart enters a checkout lane. Disadvantageously, these systems are only able to detect the presence of an object and are not able to provide any indication as to the identity of the object. Consequently, these systems cannot be integrated with the store's existing checkout subsystems and instead rely on the cashier to recognize the merchandise and input appropriate associated information, such as the identity and price of the merchandise, into the store's checkout subsystem by either bar code scanning or manual key pad entry. As such, alerts and displays for these products can only notify the cashiers of the potential existence of an item, which cashiers can ignore or defeat. Furthermore these systems do not have mechanisms to prevent collusion. In addition, disadvantageously, these infrared systems are relatively more likely to generate false positive indications. For example, these systems are unable to distinguish between merchandise located on the lower shelf of the shopping cart and a customer's bag or other personal items, again causing cashiers to eventually ignore or defeat the system by working around it.
Another supplemental device that attempts to minimize or reduce BoB losses is marketed by VerifEye Technologies <URL: http://www.verifeye.com/products/checkout/checkout.html>. This system employs a video surveillance device mounted in the lane and directed at the bottom of the basket. A small color video display is mounted near the register to aid the cashier in identifying if a BoB item exists. Again, disadvantageously, this system is not integrated with the POS, forcing reliance on the cashier to manually scan or key in the item. Consequently, the system productivity issues are ignored and collusion is not addressed. In one of VerifEye's systems, an option to log image, time and location is available making possible some analysis that could reveal losses or collusion. However, this analysis can only be performed after the fact, and therefore does not prevent a BoB loss.
As can be seen, there is a need for an improved apparatus and method that can view, recognize and automatically checkout items without a cashier's intervention, for example, when those items are located on the lower shelf of a shopping cart in the checkout lane of a retail store environment for the automated detection of merchandise.